8 citations · 14 across the 6 of their papers we have counts for
6 papers
URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios
Giuseppe Spillo, Zixuan Yi, Aleksandr Petrov +3
Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represent…
Balancing Accuracy and Novelty with Sub-Item Popularity
Chiara Mallamaci, Aleksandr Vladimirovich Petrov, Alberto Carlo Maria Mancino +3
In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repeti…
Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference laten…
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
Davide Abbattista, Vito Walter Anelli, Tommaso Di Noia +2
In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-…
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
Transformer-based recommender systems, such as BERT4Rec or SASRec, achieve state-of-the-art results in sequential recommendation. However, it is challenging to use these models in…
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Aleksandr Petrov, Craig Macdonald
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such…